In this paper, we present a novel approach to learning from visual information given in a form of raster images. The proposed learning method uses genetic programming to synthesize an image processing procedure that performs the desired vision task. The evolutionary algorithm maintains a population of individuals, initially populated with random solutions to the problem. Each individual encodes a directed acyclic graph, with graph nodes corresponding to elementary image processing operations (like image arithmetic, convolution filtering, morphological operations, etc.), and graph edges representing the data flow. Each graph contains a single input node to feed in the input image and an output node that yields the final processing result. This genetic learning process is driven by a fitness function that rewards individuals for producing output that conforms the task-specific objectives. This evaluation is carried out with respect to the training set of images. Thanks to such formulation, the fitness function is the only application-dependent component of our approach, which is thus applicable to a wide range of vision tasks (image enhancement, object detection, object tracking, etc.). The paper presents the approach in detail and describes the computational experiment concerning the task of object tracking in a real-world video sequence.
@INPROCEEDINGS { KrawiecLijewski2006,
ABSTRACT = { In this paper, we present a novel approach to learning from visual information given in a form of raster images. The proposed learning method uses genetic programming to synthesize an image processing procedure that performs the desired vision task. The evolutionary algorithm maintains a population of individuals, initially populated with random solutions to the problem. Each individual encodes a directed acyclic graph, with graph nodes corresponding to elementary image processing operations (like image arithmetic, convolution filtering, morphological operations, etc.), and graph edges representing the data flow. Each graph contains a single input node to feed in the input image and an output node that yields the final processing result. This genetic learning process is driven by a fitness function that rewards individuals for producing output that conforms the task-specific objectives. This evaluation is carried out with respect to the training set of images. Thanks to such formulation, the fitness function is the only application-dependent component of our approach, which is thus applicable to a wide range of vision tasks (image enhancement, object detection, object tracking, etc.). The paper presents the approach in detail and describes the computational experiment concerning the task of object tracking in a real-world video sequence. },
AUTHOR = { Krzysztof Krawiec and Patryk Lijewski },
BOOKTITLE = { Artificial Intelligence and Soft Computing {ICAISC} 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006. Proceedings },
FILE = { 2006ICAISCposter.pdf:./pubs/2006ICAISCposter.pdf:PDF },
PAGES = { 804--813 },
PUBLISHER = { Springer-Verlag },
SERIES = { Lecture Notes on Artificial Intelligence (LNAI) },
TITLE = { Genetic Graph Programming for Object Detection },
URL = { http://dx.doi.org/10.1007/11785231_84 },
VOLUME = { 4029 },
YEAR = { 2006 },
1 = { http://dx.doi.org/10.1007/11785231_84 },
}